Our work

Our vision is to ensure safe and effective treatment for patients affected by neglected and emerging infections. We aim to support the clinical management and research communities by providing a platform for collaboration that enables rigorous investigation of key scientific questions and rapid response to future outbreaks.

i

Credit: Mehul Dhorda, WWARN

IDDO is building on the ongoing success of the WorldWide Antimalarial Resistance Network (WWARN), a collaborative platform generating innovative resources and reliable evidence to inform the malaria community on the factors affecting the efficacy of antimalarial medicines.

IDDO aims to:

Deliver an accessible and trusted infectious disease data platform which acts as the central repository for evidence of optimal management and treatment efficacy for selected infectious diseases;

Gather and share the best clinical research practices to improve data capture and the management and integration of clinical, pharmacological/ pharmacometric and laboratory based studies;

Develop policies to establish fair conditions of use and appropriate recognition of data contributors, so that data can be made available for effective and responsible data sharing;

Support the delivery of the Sustainable Development Goals, particularly the commitment to: "… equally accelerate the pace of progress made in fighting malaria, HIV/AIDS, tuberculosis, hepatitis, Ebola and other communicable diseases and epidemics, including by addressing growing antimicrobial resistance and the problem of unattended diseases affecting developing countries.”

For each disease IDDO will:

Offer a dedicated data sharing platform and database allowing inter- and cross-disciplinary analysis of global data on efficacy of treatment;

Collate and standardise clinical, pharmacology, molecular, in vitro and medicine quality data, in collaboration with researchers;

Develop and execute a governance and ethical framework to ensure the equitable sharing of data;

Ensure the long-term security and accessibility of data so that data can be productively used for modelling and meta-analysis by the scientific community.